Overview

Dataset statistics

Number of variables13
Number of observations2966
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory301.4 KiB
Average record size in memory104.0 B

Variable types

Numeric13

Alerts

gross_revenue is highly correlated with qt_invoices and 3 other fieldsHigh correlation
recency_days is highly correlated with qt_invoicesHigh correlation
qt_invoices is highly correlated with gross_revenue and 3 other fieldsHigh correlation
no_items is highly correlated with gross_revenue and 3 other fieldsHigh correlation
assortment is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly correlated with avg_assortmentHigh correlation
avg_recency_days is highly correlated with frequencyHigh correlation
frequency is highly correlated with avg_recency_daysHigh correlation
avg_basket_size is highly correlated with gross_revenue and 1 other fieldsHigh correlation
avg_assortment is highly correlated with assortment and 1 other fieldsHigh correlation
gross_revenue is highly correlated with qt_invoices and 1 other fieldsHigh correlation
qt_invoices is highly correlated with gross_revenue and 2 other fieldsHigh correlation
no_items is highly correlated with gross_revenue and 1 other fieldsHigh correlation
assortment is highly correlated with qt_invoicesHigh correlation
avg_ticket is highly correlated with qt_returned and 1 other fieldsHigh correlation
qt_returned is highly correlated with avg_ticket and 1 other fieldsHigh correlation
avg_basket_size is highly correlated with avg_ticket and 1 other fieldsHigh correlation
gross_revenue is highly correlated with qt_invoices and 2 other fieldsHigh correlation
qt_invoices is highly correlated with gross_revenue and 2 other fieldsHigh correlation
no_items is highly correlated with gross_revenue and 3 other fieldsHigh correlation
assortment is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_recency_days is highly correlated with frequencyHigh correlation
frequency is highly correlated with avg_recency_daysHigh correlation
avg_basket_size is highly correlated with no_itemsHigh correlation
avg_assortment is highly correlated with assortmentHigh correlation
gross_revenue is highly correlated with qt_invoices and 5 other fieldsHigh correlation
qt_invoices is highly correlated with gross_revenue and 2 other fieldsHigh correlation
no_items is highly correlated with gross_revenue and 5 other fieldsHigh correlation
assortment is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_ticket is highly correlated with gross_revenue and 3 other fieldsHigh correlation
qt_returned is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_basket_size is highly correlated with gross_revenue and 3 other fieldsHigh correlation
avg_assortment is highly correlated with assortmentHigh correlation
avg_ticket is highly skewed (γ1 = 53.41722488) Skewed
qt_returned is highly skewed (γ1 = 52.67648474) Skewed
avg_basket_size is highly skewed (γ1 = 44.64531467) Skewed
df_index has unique values Unique
customer_id has unique values Unique
recency_days has 34 (1.1%) zeros Zeros
qt_returned has 1480 (49.9%) zeros Zeros

Reproduction

Analysis started2021-11-16 12:10:01.361578
Analysis finished2021-11-16 12:10:34.340894
Duration32.98 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct2966
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2309.167229
Minimum0
Maximum5690
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-16T12:10:34.472591image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile185.25
Q1926.25
median2113.5
Q33523.75
95-th percentile5011
Maximum5690
Range5690
Interquartile range (IQR)2597.5

Descriptive statistics

Standard deviation1547.505435
Coefficient of variation (CV)0.6701573693
Kurtosis-1.011941625
Mean2309.167229
Median Absolute Deviation (MAD)1265.5
Skewness0.3397296495
Sum6848990
Variance2394773.072
MonotonicityStrictly increasing
2021-11-16T12:10:34.647546image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01
 
< 0.1%
30021
 
< 0.1%
29871
 
< 0.1%
29901
 
< 0.1%
29911
 
< 0.1%
29921
 
< 0.1%
29931
 
< 0.1%
29961
 
< 0.1%
29981
 
< 0.1%
29991
 
< 0.1%
Other values (2956)2956
99.7%
ValueCountFrequency (%)
01
< 0.1%
11
< 0.1%
21
< 0.1%
31
< 0.1%
41
< 0.1%
51
< 0.1%
61
< 0.1%
71
< 0.1%
81
< 0.1%
91
< 0.1%
ValueCountFrequency (%)
56901
< 0.1%
56711
< 0.1%
56611
< 0.1%
56551
< 0.1%
56341
< 0.1%
56301
< 0.1%
56241
< 0.1%
56131
< 0.1%
56121
< 0.1%
56021
< 0.1%

customer_id
Real number (ℝ≥0)

UNIQUE

Distinct2966
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15270.64633
Minimum12347
Maximum18287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-16T12:10:34.818139image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum12347
5-th percentile12619.25
Q113799.75
median15220.5
Q316769.5
95-th percentile17964.75
Maximum18287
Range5940
Interquartile range (IQR)2969.75

Descriptive statistics

Standard deviation1719.368253
Coefficient of variation (CV)0.1125930243
Kurtosis-1.206285243
Mean15270.64633
Median Absolute Deviation (MAD)1487
Skewness0.03191154775
Sum45292737
Variance2956227.19
MonotonicityNot monotonic
2021-11-16T12:10:34.995323image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
178501
 
< 0.1%
175881
 
< 0.1%
149051
 
< 0.1%
161031
 
< 0.1%
146261
 
< 0.1%
148681
 
< 0.1%
182461
 
< 0.1%
171151
 
< 0.1%
166111
 
< 0.1%
159121
 
< 0.1%
Other values (2956)2956
99.7%
ValueCountFrequency (%)
123471
< 0.1%
123481
< 0.1%
123521
< 0.1%
123561
< 0.1%
123581
< 0.1%
123591
< 0.1%
123601
< 0.1%
123621
< 0.1%
123641
< 0.1%
123701
< 0.1%
ValueCountFrequency (%)
182871
< 0.1%
182831
< 0.1%
182821
< 0.1%
182771
< 0.1%
182761
< 0.1%
182741
< 0.1%
182731
< 0.1%
182721
< 0.1%
182701
< 0.1%
182691
< 0.1%

gross_revenue
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct2951
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2749.505877
Minimum6.2
Maximum279138.02
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-16T12:10:35.182314image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum6.2
5-th percentile230.9525
Q1571.02
median1085.51
Q32310.295
95-th percentile7226.025
Maximum279138.02
Range279131.82
Interquartile range (IQR)1739.275

Descriptive statistics

Standard deviation10565.25243
Coefficient of variation (CV)3.842600417
Kurtosis355.1539792
Mean2749.505877
Median Absolute Deviation (MAD)671.39
Skewness16.7946648
Sum8155034.43
Variance111624558.9
MonotonicityNot monotonic
2021-11-16T12:10:35.371294image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2053.022
 
0.1%
3312
 
0.1%
734.942
 
0.1%
1025.442
 
0.1%
598.22
 
0.1%
533.332
 
0.1%
731.92
 
0.1%
2092.322
 
0.1%
379.652
 
0.1%
745.062
 
0.1%
Other values (2941)2946
99.3%
ValueCountFrequency (%)
6.21
< 0.1%
13.31
< 0.1%
36.561
< 0.1%
451
< 0.1%
521
< 0.1%
52.21
< 0.1%
52.21
< 0.1%
62.431
< 0.1%
68.841
< 0.1%
70.021
< 0.1%
ValueCountFrequency (%)
279138.021
< 0.1%
259657.31
< 0.1%
194550.791
< 0.1%
168472.51
< 0.1%
136275.721
< 0.1%
124564.531
< 0.1%
116729.631
< 0.1%
91062.381
< 0.1%
72882.091
< 0.1%
66653.561
< 0.1%

recency_days
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct272
Distinct (%)9.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.19285233
Minimum0
Maximum373
Zeros34
Zeros (%)1.1%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-16T12:10:35.554475image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q111
median31
Q381
95-th percentile242
Maximum373
Range373
Interquartile range (IQR)70

Descriptive statistics

Standard deviation77.57466444
Coefficient of variation (CV)1.20846265
Kurtosis2.761679903
Mean64.19285233
Median Absolute Deviation (MAD)26
Skewness1.795006445
Sum190396
Variance6017.828563
MonotonicityNot monotonic
2021-11-16T12:10:35.754476image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
199
 
3.3%
487
 
2.9%
385
 
2.9%
284
 
2.8%
876
 
2.6%
1067
 
2.3%
766
 
2.2%
966
 
2.2%
1764
 
2.2%
1655
 
1.9%
Other values (262)2217
74.7%
ValueCountFrequency (%)
034
 
1.1%
199
3.3%
284
2.8%
385
2.9%
487
2.9%
543
1.4%
766
2.2%
876
2.6%
966
2.2%
1067
2.3%
ValueCountFrequency (%)
3732
0.1%
3723
0.1%
3711
 
< 0.1%
3681
 
< 0.1%
3664
0.1%
3652
0.1%
3641
 
< 0.1%
3601
 
< 0.1%
3591
 
< 0.1%
3584
0.1%

qt_invoices
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct57
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.723196224
Minimum1
Maximum206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-16T12:10:35.949386image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile17
Maximum206
Range205
Interquartile range (IQR)4

Descriptive statistics

Standard deviation8.845798044
Coefficient of variation (CV)1.545604536
Kurtosis190.2265924
Mean5.723196224
Median Absolute Deviation (MAD)2
Skewness10.74717886
Sum16975
Variance78.24814304
MonotonicityNot monotonic
2021-11-16T12:10:36.118607image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2785
26.5%
3497
16.8%
4394
13.3%
5236
 
8.0%
1189
 
6.4%
6173
 
5.8%
7139
 
4.7%
898
 
3.3%
969
 
2.3%
1054
 
1.8%
Other values (47)332
11.2%
ValueCountFrequency (%)
1189
 
6.4%
2785
26.5%
3497
16.8%
4394
13.3%
5236
 
8.0%
6173
 
5.8%
7139
 
4.7%
898
 
3.3%
969
 
2.3%
1054
 
1.8%
ValueCountFrequency (%)
2061
< 0.1%
1981
< 0.1%
1241
< 0.1%
971
< 0.1%
911
< 0.1%
901
< 0.1%
861
< 0.1%
721
< 0.1%
622
0.1%
601
< 0.1%

no_items
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1677
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1610.217465
Minimum2
Maximum196844
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-16T12:10:36.300401image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile103
Q1297
median642
Q31401
95-th percentile4408
Maximum196844
Range196842
Interquartile range (IQR)1104

Descriptive statistics

Standard deviation5889.962303
Coefficient of variation (CV)3.657867606
Kurtosis465.6488492
Mean1610.217465
Median Absolute Deviation (MAD)422.5
Skewness17.85151444
Sum4775905
Variance34691655.93
MonotonicityNot monotonic
2021-11-16T12:10:36.480453image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31011
 
0.4%
889
 
0.3%
1509
 
0.3%
2888
 
0.3%
2728
 
0.3%
2608
 
0.3%
2468
 
0.3%
1348
 
0.3%
848
 
0.3%
2197
 
0.2%
Other values (1667)2882
97.2%
ValueCountFrequency (%)
22
0.1%
122
0.1%
161
< 0.1%
171
< 0.1%
181
< 0.1%
191
< 0.1%
201
< 0.1%
231
< 0.1%
251
< 0.1%
261
< 0.1%
ValueCountFrequency (%)
1968441
< 0.1%
809971
< 0.1%
801791
< 0.1%
773731
< 0.1%
699931
< 0.1%
645491
< 0.1%
641241
< 0.1%
633121
< 0.1%
583431
< 0.1%
578721
< 0.1%

assortment
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct467
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.7697235
Minimum1
Maximum7838
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-16T12:10:36.808847image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9
Q129
median67
Q3135
95-th percentile382
Maximum7838
Range7837
Interquartile range (IQR)106

Descriptive statistics

Standard deviation269.4103935
Coefficient of variation (CV)2.194436753
Kurtosis354.1904831
Mean122.7697235
Median Absolute Deviation (MAD)44
Skewness15.67478035
Sum364135
Variance72581.96011
MonotonicityNot monotonic
2021-11-16T12:10:36.991297image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2844
 
1.5%
2037
 
1.2%
3535
 
1.2%
2934
 
1.1%
1533
 
1.1%
1933
 
1.1%
1132
 
1.1%
2531
 
1.0%
2630
 
1.0%
2730
 
1.0%
Other values (457)2627
88.6%
ValueCountFrequency (%)
15
 
0.2%
214
0.5%
316
0.5%
417
0.6%
526
0.9%
628
0.9%
718
0.6%
819
0.6%
926
0.9%
1028
0.9%
ValueCountFrequency (%)
78381
< 0.1%
55891
< 0.1%
50951
< 0.1%
45801
< 0.1%
26971
< 0.1%
23791
< 0.1%
20601
< 0.1%
18181
< 0.1%
16721
< 0.1%
16371
< 0.1%

avg_ticket
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1993
Distinct (%)67.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.92923803
Minimum2.15
Maximum56157.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-16T12:10:37.192153image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum2.15
5-th percentile4.915
Q113.12
median17.94
Q324.9875
95-th percentile90.5025
Maximum56157.5
Range56155.35
Interquartile range (IQR)11.8675

Descriptive statistics

Standard deviation1037.458389
Coefficient of variation (CV)19.9783095
Kurtosis2887.786898
Mean51.92923803
Median Absolute Deviation (MAD)5.97
Skewness53.41722488
Sum154022.12
Variance1076319.91
MonotonicityNot monotonic
2021-11-16T12:10:37.369533image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15.497
 
0.2%
16.826
 
0.2%
16.396
 
0.2%
16.926
 
0.2%
19.066
 
0.2%
17.666
 
0.2%
17.716
 
0.2%
19.445
 
0.2%
105
 
0.2%
17.135
 
0.2%
Other values (1983)2908
98.0%
ValueCountFrequency (%)
2.151
< 0.1%
2.431
< 0.1%
2.461
< 0.1%
2.511
< 0.1%
2.521
< 0.1%
2.651
< 0.1%
2.661
< 0.1%
2.711
< 0.1%
2.761
< 0.1%
2.771
< 0.1%
ValueCountFrequency (%)
56157.51
< 0.1%
4453.431
< 0.1%
3202.921
< 0.1%
1687.21
< 0.1%
952.991
< 0.1%
872.131
< 0.1%
841.021
< 0.1%
651.171
< 0.1%
6401
< 0.1%
624.41
< 0.1%

avg_recency_days
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1258
Distinct (%)42.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.37893552
Minimum1
Maximum366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-16T12:10:37.542845image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile8
Q126
median48.39285714
Q385.33333333
95-th percentile201
Maximum366
Range365
Interquartile range (IQR)59.33333333

Descriptive statistics

Standard deviation63.56115532
Coefficient of variation (CV)0.9433386685
Kurtosis4.882651397
Mean67.37893552
Median Absolute Deviation (MAD)26.27380952
Skewness2.062289986
Sum199845.9227
Variance4040.020465
MonotonicityNot monotonic
2021-11-16T12:10:37.734077image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1425
 
0.8%
7021
 
0.7%
421
 
0.7%
720
 
0.7%
3518
 
0.6%
4918
 
0.6%
4617
 
0.6%
2117
 
0.6%
1117
 
0.6%
2816
 
0.5%
Other values (1248)2776
93.6%
ValueCountFrequency (%)
116
0.5%
1.51
 
< 0.1%
213
0.4%
2.51
 
< 0.1%
2.6013986011
 
< 0.1%
315
0.5%
3.3214285711
 
< 0.1%
3.3303571431
 
< 0.1%
3.52
 
0.1%
421
0.7%
ValueCountFrequency (%)
3661
 
< 0.1%
3651
 
< 0.1%
3631
 
< 0.1%
3621
 
< 0.1%
3572
0.1%
3561
 
< 0.1%
3552
0.1%
3521
 
< 0.1%
3512
0.1%
3503
0.1%

frequency
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION

Distinct1348
Distinct (%)45.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.06316555961
Minimum0.005449591281
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-16T12:10:37.918960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0.005449591281
5-th percentile0.009433962264
Q10.01777777778
median0.02929636612
Q30.05531929419
95-th percentile0.2222222222
Maximum3
Range2.994550409
Interquartile range (IQR)0.03754151641

Descriptive statistics

Standard deviation0.1344005805
Coefficient of variation (CV)2.127750967
Kurtosis122.000165
Mean0.06316555961
Median Absolute Deviation (MAD)0.01427006162
Skewness8.794847332
Sum187.3490498
Variance0.01806351605
MonotonicityNot monotonic
2021-11-16T12:10:38.101359image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.333333333321
 
0.7%
0.166666666721
 
0.7%
0.0277777777820
 
0.7%
0.0909090909119
 
0.6%
0.062517
 
0.6%
0.133333333316
 
0.5%
0.415
 
0.5%
0.0357142857115
 
0.5%
0.2515
 
0.5%
0.0238095238115
 
0.5%
Other values (1338)2792
94.1%
ValueCountFrequency (%)
0.0054495912811
 
< 0.1%
0.0054644808741
 
< 0.1%
0.0054945054951
 
< 0.1%
0.0055096418731
 
< 0.1%
0.0055865921792
0.1%
0.0056022408961
 
< 0.1%
0.0056179775282
0.1%
0.005665722381
 
< 0.1%
0.0056818181822
0.1%
0.0056980056983
0.1%
ValueCountFrequency (%)
31
 
< 0.1%
21
 
< 0.1%
1.5714285711
 
< 0.1%
1.53
 
0.1%
114
0.5%
0.83333333331
 
< 0.1%
0.751
 
< 0.1%
0.666666666712
0.4%
0.64879356571
 
< 0.1%
0.61
 
< 0.1%

qt_returned
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct173
Distinct (%)5.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.25556305
Minimum0
Maximum80995
Zeros1480
Zeros (%)49.9%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-16T12:10:38.288854image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q36
95-th percentile62.75
Maximum80995
Range80995
Interquartile range (IQR)6

Descriptive statistics

Standard deviation1504.24228
Coefficient of variation (CV)28.78626106
Kurtosis2830.792868
Mean52.25556305
Median Absolute Deviation (MAD)1
Skewness52.67648474
Sum154990
Variance2262744.836
MonotonicityNot monotonic
2021-11-16T12:10:38.483233image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01480
49.9%
1295
 
9.9%
3169
 
5.7%
693
 
3.1%
286
 
2.9%
471
 
2.4%
543
 
1.4%
1243
 
1.4%
840
 
1.3%
738
 
1.3%
Other values (163)608
20.5%
ValueCountFrequency (%)
01480
49.9%
1295
 
9.9%
286
 
2.9%
3169
 
5.7%
471
 
2.4%
543
 
1.4%
693
 
3.1%
738
 
1.3%
840
 
1.3%
936
 
1.2%
ValueCountFrequency (%)
809951
< 0.1%
90141
< 0.1%
48241
< 0.1%
40271
< 0.1%
23022
0.1%
17761
< 0.1%
16081
< 0.1%
15891
< 0.1%
15151
< 0.1%
12781
< 0.1%

avg_basket_size
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED

Distinct1980
Distinct (%)66.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean250.0850739
Minimum1
Maximum40498.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-16T12:10:38.681178image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile44.29166667
Q1103.3083333
median172.3809524
Q3281.9230769
95-th percentile600
Maximum40498.5
Range40497.5
Interquartile range (IQR)178.6147436

Descriptive statistics

Standard deviation791.9796007
Coefficient of variation (CV)3.166840741
Kurtosis2252.924831
Mean250.0850739
Median Absolute Deviation (MAD)83
Skewness44.64531467
Sum741752.3291
Variance627231.6879
MonotonicityNot monotonic
2021-11-16T12:10:38.918129image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10011
 
0.4%
11410
 
0.3%
829
 
0.3%
739
 
0.3%
869
 
0.3%
1408
 
0.3%
608
 
0.3%
1368
 
0.3%
758
 
0.3%
888
 
0.3%
Other values (1970)2878
97.0%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
3.3333333331
< 0.1%
5.3333333331
< 0.1%
5.6666666671
< 0.1%
6.1428571431
< 0.1%
7.51
< 0.1%
91
< 0.1%
9.51
< 0.1%
111
< 0.1%
ValueCountFrequency (%)
40498.51
< 0.1%
6009.3333331
< 0.1%
42821
< 0.1%
39061
< 0.1%
3868.651
< 0.1%
28801
< 0.1%
28011
< 0.1%
2733.9444441
< 0.1%
2518.7692311
< 0.1%
2160.3333331
< 0.1%

avg_assortment
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct1006
Distinct (%)33.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.17083728
Minimum1
Maximum299.7058824
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.3 KiB
2021-11-16T12:10:39.105979image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.480263158
Q110
median17.2
Q327.75
95-th percentile57
Maximum299.7058824
Range298.7058824
Interquartile range (IQR)17.75

Descriptive statistics

Standard deviation19.52296715
Coefficient of variation (CV)0.8805696829
Kurtosis27.65907366
Mean22.17083728
Median Absolute Deviation (MAD)8.2
Skewness3.497535954
Sum65758.70338
Variance381.1462465
MonotonicityNot monotonic
2021-11-16T12:10:39.288960image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1354
 
1.8%
1439
 
1.3%
1138
 
1.3%
933
 
1.1%
1832
 
1.1%
1731
 
1.0%
131
 
1.0%
2031
 
1.0%
1030
 
1.0%
1629
 
1.0%
Other values (996)2618
88.3%
ValueCountFrequency (%)
131
1.0%
1.21
 
< 0.1%
1.251
 
< 0.1%
1.3333333332
 
0.1%
1.58
 
0.3%
1.5681818181
 
< 0.1%
1.5714285711
 
< 0.1%
1.6666666674
 
0.1%
1.8333333331
 
< 0.1%
224
0.8%
ValueCountFrequency (%)
299.70588241
< 0.1%
2591
< 0.1%
203.51
< 0.1%
1481
< 0.1%
1451
< 0.1%
136.1251
< 0.1%
135.51
< 0.1%
1271
< 0.1%
1221
< 0.1%
1181
< 0.1%

Interactions

2021-11-16T12:10:31.550663image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:03.705414image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:06.054237image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:08.242224image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:10.765919image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:13.189997image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:15.422419image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:17.935572image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:20.191433image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:22.503743image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:24.767310image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:27.011513image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:29.215172image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:31.707991image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:03.915568image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:06.219974image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:08.407548image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:10.931519image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:13.485031image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:15.577839image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:18.111200image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:20.354984image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:22.672875image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:24.906827image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:27.172687image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:29.371484image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:31.861435image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:04.069846image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:06.389469image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:08.551308image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:11.109064image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:13.644925image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:15.761791image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:18.286137image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:20.525666image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:22.821435image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:25.071788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:27.342794image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:29.527659image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:32.003606image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:04.243973image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:06.556442image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:08.718805image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:11.297051image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:13.813490image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:15.941501image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:18.454470image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:20.726598image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:22.979922image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:25.219394image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:27.503348image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:29.822493image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:32.183887image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:04.424905image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:06.747267image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:09.031832image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:11.464893image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:13.981428image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:16.136529image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:18.649054image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:20.901799image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:23.135484image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:25.383022image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:27.668443image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:29.997821image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:32.326066image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:04.563877image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:06.899232image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:09.214650image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:11.634326image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:14.150100image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:16.306570image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:18.795317image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:21.070585image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:23.279394image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:25.536582image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:27.818176image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:30.153620image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:32.508528image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:04.902321image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:07.059740image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:09.400387image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:11.893978image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:14.322813image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:16.502322image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:18.972214image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:21.245563image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:23.449060image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:25.856140image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:28.001647image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:30.333887image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:32.679188image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:05.089247image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:07.230262image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:09.642405image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:12.076162image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:14.484267image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:16.682518image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:19.161681image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:21.410803image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:23.656904image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:26.036562image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:28.183188image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:30.519995image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:32.838596image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:05.232924image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:07.403754image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:09.838751image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:12.246833image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:14.646799image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:16.831780image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:19.339744image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:21.567889image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:23.839547image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:26.189439image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:28.341594image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:30.693642image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:33.006753image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:05.391387image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:07.582830image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:10.035745image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:12.411763image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:14.805868image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:17.000124image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:19.518273image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:21.876865image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:24.038802image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:26.358331image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:28.509196image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:30.863575image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:33.166588image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:05.535194image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:07.751172image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:10.231027image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:12.593724image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:14.958052image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:17.190297image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:19.683244image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:22.031252image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:24.221057image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:26.522517image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:28.677083image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:31.032334image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:33.344612image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:05.700681image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:07.913059image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:10.410226image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:12.819849image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:15.118423image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:17.365259image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:19.858014image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:22.195813image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:24.392288image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:26.684349image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:28.847784image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:31.211493image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:33.506429image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:05.872523image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:08.084652image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:10.598656image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:13.021931image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:15.273344image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:17.755788image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:20.030326image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:22.351779image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:24.605157image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:26.842940image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:29.034050image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
2021-11-16T12:10:31.391122image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Correlations

2021-11-16T12:10:39.640092image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-11-16T12:10:39.879147image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-11-16T12:10:40.119512image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-11-16T12:10:40.348413image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-11-16T12:10:33.941637image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
A simple visualization of nullity by column.
2021-11-16T12:10:34.232422image/svg+xmlMatplotlib v3.4.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

df_indexcustomer_idgross_revenuerecency_daysqt_invoicesno_itemsassortmentavg_ticketavg_recency_daysfrequencyqt_returnedavg_basket_sizeavg_assortment
00178505391.21372.034.01733.0297.018.1535.5000000.48611121.050.9705888.735294
11130473232.5956.09.01390.0171.018.9027.2500000.0487806.0154.44444419.000000
22125836705.382.015.05028.0232.028.9023.1875000.04569950.0335.20000015.466667
3313748948.2595.05.0439.028.033.8792.6666670.0179210.087.8000005.600000
4415100876.00333.03.080.03.0292.008.6000000.13636422.026.6666671.000000
55152914623.3025.014.02102.0102.045.3323.2000000.05444127.0150.1428577.285714
66146885630.877.021.03621.0327.017.2218.3000000.073569281.0172.42857115.571429
77178095411.9116.012.02057.061.088.7235.7000000.03910641.0171.4166675.083333
881531160767.900.091.038194.02379.025.544.1444440.315508231.0419.71428626.142857
99160982005.6387.07.0613.067.029.9347.6666670.0243900.087.5714299.571429

Last rows

df_indexcustomer_idgross_revenuerecency_daysqt_invoicesno_itemsassortmentavg_ticketavg_recency_daysfrequencyqt_returnedavg_basket_sizeavg_assortment
29565602177271060.2515.01.0645.066.016.066.00.2857146.0645.00000066.0
2957561217232421.522.02.0203.036.011.7112.00.1538460.0101.50000018.0
2958561317468137.0010.02.0116.05.027.404.00.4000000.058.0000002.5
2959562413596697.045.02.0406.0166.04.207.00.2500000.0203.00000083.0
29605630148931237.859.02.0799.073.016.962.00.6666670.0399.50000036.5
2961563412479473.2011.01.0382.030.015.774.00.33333334.0382.00000030.0
2962565514126706.137.03.0508.015.047.083.01.00000050.0169.3333335.0
29635661135211092.391.03.0733.0435.02.514.50.3000000.0244.333333145.0
2964567115060301.848.04.0262.0120.02.521.02.0000000.065.50000030.0
2965569012558269.967.01.0196.011.024.546.00.285714102.0196.00000011.0